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The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model.

The model is developed with flight velocity, ambient temperature, liquid water content, median volumetric diameter and icing time taken as inputs and icing thickness given as outputs. To enhance the icing prediction accuracy, the model involves geometrical constraints into the loss function. Then the model is trained according to icing samples of 2D NACA0012 airfoil acquired by numerical simulation.

The results show that the involvement of geometrical constraints effectively enhances the prediction accuracy of ice shape, by weakening the appearance of fluctuation features. After training, the airfoil icing prediction model can be used for quickly predicting airfoil icing.

This work involves geometrical constraints in airfoil icing prediction model. The proposed model has reasonable capability in the fast assessment of aircraft icing.

The volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.

The synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.

Utilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.

The construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.

Conducting innovation in low-carbon technology is vital to boost the low-carbon economy. Collaborative innovation among industry-university-research (IUR) is an effective mode of developing low-carbon technologies. There is a lack of visualization and analysis of the spatial-temporal of such collaboration among the IUR. This paper aims to serve as insights to guide IUR’s collaborative innovation in Chinese universities to promote low-carbon technologies.

This paper uses IncoPat to collect patent data. Collaborative patent output on low-carbon technologies was selected as the indicator to measure the effectiveness of IUR collaboration. The temporal evolution trend of the collaborative patent output in Chinese universities is analyzed.

The collaborative patent output of the Chinese IUR varies greatly among the regions, evolving from stronger in the east and weaker in the west, to stronger in the south and weaker in the north region. The triple helix (TH) innovation system in China’s low-carbon sector is dominated by intraregional collaborative innovation, while there is a weak bilateral synergy between universities and research institutions.

This paper innovatively developed a novel TH model that characterize the regional differences of the IUR collaboration in low-carbon technology innovation of Chinese universities. A new attempt focuses on the spatial-temporal evolution of the collaborative innovation of IUR to promote low-carbon technologies.

This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes.

The proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose.

Comprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach.

This paper provides comprehensive experiments in real industrial environments.

The system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios.

As China is on an inevitable march into the digital era, firms have accumulated abundant digital assets, such as algorithms and data. Facing the possibility of using digital assets as a new type input, besides traditional inputs such as capital and labor, would powerful managers perform better? Would managerial power help managers increase the efficiency of how a firm combines traditional and digital inputs and converts them into outputs? Thus, the purpose of this study is to investigate whether powerful managers promotes corporate productivity by using digital assets as a new input.

Using data from listed Chinese firms between 2008 and 2020, the authors constructed panel regressions with three-way fixed effects to examine whether and how managerial power influences corporate productivity in the current digital context, particularly under market uncertainty.

The findings reveal no consistent relationship between managerial power and corporate productivity. The results explain this from two contrasting effects: while managerial power promotes technological change it hinders technical efficiency – two components of total productivity. Moreover, this study identifies market uncertainty as a significant external contingency. In uncertain markets, strong managerial power positively impacts corporate productivity.

The results extend extant theoretical insights in the literature on how managerial power might influence corporate productivity.

This chapter sets out to analyze the impact of the Covid-19 virus on the holidays provided by UK group tour operators (GTOs) and the implications for overtourism. With tourism arrivals expected to fall by up to 30% in 2020 and a slow return to pre-Covid-19 levels for 2021 and beyond, the industry is possibly suffering the loss of up to 100 million travel-related jobs (World Travel and Tourism Council, 2020). GTOs will need to assess and possibly change the way they do business to initially survive and subsequently build up tourism numbers in the coming years.

This chapter identifies how GTOs could alter their holiday proposition to reassure travellers including the challenges of operating international tours when airlines have reduced capacity, the need to consider alternative age demographics who are more likely to travel and assessing existing itineraries to visit rural or small town locations rather than cities where numerous itineraries travel to now.

Finally, this chapter discusses and describes the significance of the findings with insights about possible opportunities based upon the approaches taken by various countries to target potential holidaymakers and the need to create a ‘crisis management plan’ for current and future countries. This may result in operational adjustments to meet these new requirements including the changing outlook of potential customers and the possibility of offering domestic tours to meet the current demand.

This paper aims to explore the evolution mechanism of resources in a standard alliance that are matched with resources required at different standardization stages from the viewpoint of dynamic matching. How core enterprises in an alliance allocate resources, select member enterprises and maintain the normal operation of an alliance, according to the resource evolution of a standard alliance, is an important issue when dealing with the implementation of technology standardization.

The authors have chosen the Intelligent Grouping and Resource Sharing (IGRS) standard alliance of computer companies in China as the object of this study. The authors have built indices to identify core enterprises in the alliance from the viewpoint of network organization. The authors also collected data from authoritative news websites concerning patents and cooperative projects undertaken by 216 enterprises in the IGRS alliance during the period from 2002 to 2016, and they have computed and analyzed these data by using UCINET 6.0 software and social network analysis methodology to identify core enterprises at different standardization stages, thus revealing the evolution mechanism for resources in the standard alliance.

Technology standardization is divided into R&D, industrialization and marketization stages, and the standard alliance requires different resources to satisfy what is required at each of those different standardization stages. While technology standardization is a process during which technology systems standards are continuously being perfected and the standard product market is continuously expanding, the development of technology standardization affects the evolutionary processes of the core enterprises and affects the selection of member enterprises in the standard alliance.

The results obtained will assist the standard alliance to select proper member enterprises and dynamically match the alliance’s resources with the resources required at different standardization stages to speed up the implementation of independent standardization in China.

This study demonstrates the evolution mechanism of resources in technology standard alliances at different standardization stages by using quantitative analysis methodology, and it enriches the research on which elements are influential for technology standardization’s development in the context of China’s social, economic and cultural characteristics.